Pemulusan Eksponensial Sederhana
import japanize_matplotlib as jm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.tsa.holtwinters import SimpleExpSmoothing
jm.japanize()
Data Sampel
Data yang digunakan adalah Natural Gas Consumption (NATURALGAS). Satuan angka adalah BCF (miliar kaki kubik).
data = {
"value": {
"2013-01-01": 2878.8,
"2013-02-01": 2567.2,
"2013-03-01": 2521.1,
"2013-04-01": 1967.5,
"2013-05-01": 1752.5,
"2013-06-01": 1742.9,
"2013-07-01": 1926.3,
"2013-08-01": 1927.4,
"2013-09-01": 1767.0,
"2013-10-01": 1866.8,
"2013-11-01": 2316.9,
"2013-12-01": 2920.8,
"2014-01-01": 3204.1,
"2014-02-01": 2741.2,
"2014-03-01": 2557.9,
"2014-04-01": 1961.7,
"2014-05-01": 1810.2,
"2014-06-01": 1745.4,
"2014-07-01": 1881.0,
"2014-08-01": 1933.1,
"2014-09-01": 1809.3,
"2014-10-01": 1912.8,
"2014-11-01": 2357.5,
"2014-12-01": 2679.2,
"2015-01-01": 3115.0,
"2015-02-01": 2925.2,
"2015-03-01": 2591.3,
"2015-04-01": 2007.9,
"2015-05-01": 1858.2,
"2015-06-01": 1899.9,
"2015-07-01": 2067.7,
"2015-08-01": 2052.7,
"2015-09-01": 1901.3,
"2015-10-01": 1987.3,
"2015-11-01": 2249.1,
"2015-12-01": 2588.2,
"2016-01-01": 3091.7,
"2016-02-01": 2652.3,
"2016-03-01": 2356.3,
"2016-04-01": 2083.9,
"2016-05-01": 1965.8,
"2016-06-01": 2000.7,
"2016-07-01": 2186.6,
"2016-08-01": 2208.4,
"2016-09-01": 1947.8,
"2016-10-01": 1925.2,
"2016-11-01": 2159.5,
"2016-12-01": 2866.3,
"2017-01-01": 2913.8,
"2017-02-01": 2340.2,
"2017-03-01": 2523.3,
"2017-04-01": 1932.0,
"2017-05-01": 1892.0,
"2017-06-01": 1910.4,
"2017-07-01": 2141.6,
"2017-08-01": 2093.8,
"2017-09-01": 1920.5,
"2017-10-01": 2031.5,
"2017-11-01": 2357.3,
"2017-12-01": 3086.0,
"2018-01-01": 3340.9,
"2018-02-01": 2710.7,
"2018-03-01": 2796.7,
"2018-04-01": 2350.5,
"2018-05-01": 2055.0,
"2018-06-01": 2063.1,
"2018-07-01": 2350.7,
"2018-08-01": 2313.8,
"2018-09-01": 2156.1,
"2018-10-01": 2285.9,
"2018-11-01": 2715.9,
"2018-12-01": 2999.5,
"2019-01-01": 3424.3,
"2019-02-01": 3019.1,
"2019-03-01": 2927.8,
"2019-04-01": 2212.4,
"2019-05-01": 2134.0,
"2019-06-01": 2119.3,
"2019-07-01": 2393.9,
"2019-08-01": 2433.9,
"2019-09-01": 2206.3,
"2019-10-01": 2306.5,
"2019-11-01": 2783.8,
"2019-12-01": 3170.7,
"2020-01-01": 3320.6,
"2020-02-01": 3058.5,
"2020-03-01": 2722.0,
"2020-04-01": 2256.9,
"2020-05-01": 2072.2,
"2020-06-01": 2127.9,
"2020-07-01": 2464.1,
"2020-08-01": 2399.5,
"2020-09-01": 2151.2,
"2020-10-01": 2315.9,
"2020-11-01": 2442.0,
"2020-12-01": 3182.8,
"2021-01-01": 3343.9,
"2021-02-01": 3099.2,
"2021-03-01": 2649.4,
"2021-04-01": 2265.1,
"2021-05-01": 2117.4,
"2021-06-01": 2238.4,
"2021-07-01": 2412.2,
"2021-08-01": 2433.8,
"2021-09-01": 2142.3,
"2021-10-01": 2262.6,
"2021-11-01": 2693.3,
"2021-12-01": 3007.3,
"2022-01-01": 3612.1,
"2022-02-01": 3064.2,
"2022-03-01": 2785.4,
"2022-04-01": 2379.3,
"2022-05-01": 2247.8,
"2022-06-01": 2326.9,
"2022-07-01": 2597.9,
"2022-08-01": 2566.1,
"2022-09-01": 2263.3,
}
}
data = pd.DataFrame(data)
data.rename(columns={"value": "Natural Gas Consumption(BCF)"}, inplace=True)
data.index = pd.to_datetime(data.index)
data = data.asfreq("MS")
data.head()
| Natural Gas Consumption(BCF) | |
|---|---|
| 2013-01-01 | 2878.8 |
| 2013-02-01 | 2567.2 |
| 2013-03-01 | 2521.1 |
| 2013-04-01 | 1967.5 |
| 2013-05-01 | 1752.5 |
plt.figure(figsize=(12, 6))
sns.lineplot(data=data)
plt.grid()
plt.show()
Pembagian Data
Data akan dibagi, di mana data bagian akhir (data setelah tahun 2020) akan digunakan untuk memverifikasi kinerja prediksi.
data_train = data[data.index < "2020-1-1"]
data_test = data[data.index >= "2020-1-1"]
plt.figure(figsize=(12, 6))
plt.plot(data_train.index, data_train.values, label="train", linewidth=2)
plt.plot(data_test.index, data_test.values, "-.", label="test")
plt.legend()
plt.grid()
Pelatihan dan Prediksi Model
ses = SimpleExpSmoothing(data_train)
ses = ses.fit(smoothing_level=0.1)
pred = ses.forecast(33)
plt.figure(figsize=(12, 6))
plt.plot(data_train.index, data_train.values, label="train", linewidth=2)
plt.plot(data_test.index, data_test.values, "-.", label="test")
plt.plot(pred.index, pred.values, "-x", label="pred")
plt.legend()
plt.grid()
